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Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances

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Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances. / McKinley, Jennifer M.; Mueller, Ute; Atkinson, Peter; Ofterdinger, Ulrich; Jackson, Chloe; Cox, Siobhan F.; Doherty, Rory; Fogarty, Damian; Egozcue, J.J.; Pawlowsky-Glahn, V.

In: Applied Computing and Geosciences, Vol. 6, 100024, 01.06.2020.

Research output: Contribution to journalJournal articlepeer-review

Harvard

McKinley, JM, Mueller, U, Atkinson, P, Ofterdinger, U, Jackson, C, Cox, SF, Doherty, R, Fogarty, D, Egozcue, JJ & Pawlowsky-Glahn, V 2020, 'Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances', Applied Computing and Geosciences, vol. 6, 100024. https://doi.org/10.1016/j.acags.2020.100024

APA

McKinley, J. M., Mueller, U., Atkinson, P., Ofterdinger, U., Jackson, C., Cox, S. F., Doherty, R., Fogarty, D., Egozcue, J. J., & Pawlowsky-Glahn, V. (2020). Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances. Applied Computing and Geosciences, 6, [100024]. https://doi.org/10.1016/j.acags.2020.100024

Vancouver

Author

McKinley, Jennifer M. ; Mueller, Ute ; Atkinson, Peter ; Ofterdinger, Ulrich ; Jackson, Chloe ; Cox, Siobhan F. ; Doherty, Rory ; Fogarty, Damian ; Egozcue, J.J. ; Pawlowsky-Glahn, V. / Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances. In: Applied Computing and Geosciences. 2020 ; Vol. 6.

Bibtex

@article{49fd53004ec5499399197302506fabaf,
title = "Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances",
abstract = "This research uses an urban soil geochemistry database of elemental concentration to examine the potential relationship between Standardised Incidence Rates (SIRs) of Chronic Kidney Disease (CKD) of uncertain aetiology (CKDu), and cumulative low level geogenic and diffuse anthropogenic contamination of soils with PTEs. A compositional data analysis approach was applied to determine the elemental balance(s) of the geochemical data showing the greatest association with CKDu. The research concludes that both anthropogenic and geogenic factors may be contributing influences to explain high incidences of CKDu, up to 12 times greater in some Super Output Areas (SOA) than would be expected for the average population. The role of As, Cr, Cu, Pb, Sb and Mo was highlighted, which may be linked to anthropogenic sources such as historical industrial sources, atmospheric pollution deposition and brake emissions. Geogenic factors were shown to be important in areas with elevated relative concentrations of naturally occurring potentially toxic elements (PTE). ",
keywords = "Geochemistry, chronic kidney disease, uncertain aetiology",
author = "McKinley, {Jennifer M.} and Ute Mueller and Peter Atkinson and Ulrich Ofterdinger and Chloe Jackson and Cox, {Siobhan F.} and Rory Doherty and Damian Fogarty and J.J. Egozcue and V. Pawlowsky-Glahn",
year = "2020",
month = jun,
day = "1",
doi = "10.1016/j.acags.2020.100024",
language = "English",
volume = "6",
journal = "Applied Computing and Geosciences",
issn = "2590-1974",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances

AU - McKinley, Jennifer M.

AU - Mueller, Ute

AU - Atkinson, Peter

AU - Ofterdinger, Ulrich

AU - Jackson, Chloe

AU - Cox, Siobhan F.

AU - Doherty, Rory

AU - Fogarty, Damian

AU - Egozcue, J.J.

AU - Pawlowsky-Glahn, V.

PY - 2020/6/1

Y1 - 2020/6/1

N2 - This research uses an urban soil geochemistry database of elemental concentration to examine the potential relationship between Standardised Incidence Rates (SIRs) of Chronic Kidney Disease (CKD) of uncertain aetiology (CKDu), and cumulative low level geogenic and diffuse anthropogenic contamination of soils with PTEs. A compositional data analysis approach was applied to determine the elemental balance(s) of the geochemical data showing the greatest association with CKDu. The research concludes that both anthropogenic and geogenic factors may be contributing influences to explain high incidences of CKDu, up to 12 times greater in some Super Output Areas (SOA) than would be expected for the average population. The role of As, Cr, Cu, Pb, Sb and Mo was highlighted, which may be linked to anthropogenic sources such as historical industrial sources, atmospheric pollution deposition and brake emissions. Geogenic factors were shown to be important in areas with elevated relative concentrations of naturally occurring potentially toxic elements (PTE).

AB - This research uses an urban soil geochemistry database of elemental concentration to examine the potential relationship between Standardised Incidence Rates (SIRs) of Chronic Kidney Disease (CKD) of uncertain aetiology (CKDu), and cumulative low level geogenic and diffuse anthropogenic contamination of soils with PTEs. A compositional data analysis approach was applied to determine the elemental balance(s) of the geochemical data showing the greatest association with CKDu. The research concludes that both anthropogenic and geogenic factors may be contributing influences to explain high incidences of CKDu, up to 12 times greater in some Super Output Areas (SOA) than would be expected for the average population. The role of As, Cr, Cu, Pb, Sb and Mo was highlighted, which may be linked to anthropogenic sources such as historical industrial sources, atmospheric pollution deposition and brake emissions. Geogenic factors were shown to be important in areas with elevated relative concentrations of naturally occurring potentially toxic elements (PTE).

KW - Geochemistry

KW - chronic kidney disease

KW - uncertain aetiology

U2 - 10.1016/j.acags.2020.100024

DO - 10.1016/j.acags.2020.100024

M3 - Journal article

VL - 6

JO - Applied Computing and Geosciences

JF - Applied Computing and Geosciences

SN - 2590-1974

M1 - 100024

ER -